| 摘要: |
| 【目的】改善侧向进水泵站前池内的水流流态和水泵进水条件。【方法】采用计算流体动力学（CFD）和BPNN-GA（Back Propagation Neural Network-Genetic Algorithm）算法，对泵站前池的整流斜板结构设计参数进行优化。通过引入基于轴向流速分布均匀度和速度加权平均角的综合评价指标F，使用遗传算法优化BPNN模型，以获取最优整流斜板结构设计参数。【结果】通过BPNN-GA算法优化的整流斜板可有效改善进水流道内的水流流态，轴向流速分布均匀度以及速度加权平均角得到较大提高，前池内部漩涡范围明显缩小，综合评价指标F下降了6.31。【结论】因此，BPNN-GA算法可以高效地选择出整流斜板最优结构设计参数，可改善侧向进水泵站前池内不良流态。 |
| 关键词: 泵站；侧向进水；BP神经网络；遗传算法；整流斜板 |
| DOI：10.13522/j.cnki.ggps.2023481 |
| 分类号: |
| 基金项目: |
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| Optimizing baffle plates in the forebay of lateral inlet pump station using backpropagation neural network-genetic algorithm |
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YAN Haodi, YU Yonghai
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College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
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| Abstract: |
| 【Objective】The baffle plates in the forebay of lateral inlet pump stations are critical components to control water flow and optimize the performance of the pump station. We proposed a method to optimize their design.【Method】We used computational fluid dynamics (CFD) combined with the backpropagation neural network-genetic algorithm (BPNN-GA) to optimize the design parameters of the rectification baffle plates in the forebay. The optimization process used a comprehensive evaluation index, F, which calculated the fitness of the genetic algorithm based on the uniformity of the axial flow velocity distribution and the velocity-weighted average angle. The BPNN model was fine-tuned, with the comprehensive index F as the optimization objective, leading to the identification of optimal design parameters for the rectification baffle plates.【Result】The optimization results showed that the rectification baffle plates designed by the BPNN-GA algorithm significantly improved the flow pattern within the inlet channel. Notable improvements included increased uniformity in axial velocity distribution and a better velocity-weighted average angle. Additionally, the improved design also significantly reduced the vortex area within the forebay, with the comprehensive evaluation index F showing a 6.31 reduction.【Conclusion】The proposed BPNN-GA algorithm for optimizing the design parameters of the rectification baffle plates effectively ameliorated the undesirable flow conditions in the forebay. It provides a valuable method for improving the design of similar hydraulic structures. |
| Key words: pump station; lateral inflow; BP neural network; genetic algorithm; rectification baffle plate |
